def main():
sess = tf.Session()
image = read_image('../data/heart.jpg')
image = np.reshape(image, [1, 224, 224, 3]) # type numpy.ndarray
image.astype(np.float32)
parser = Parser('../data/alexnet.cfg')
network_builder = NetworkBuilder("test") # type: NetworkBuilder
network_builder.set_parser(parser)
network = network_builder.build() # type: Network
network.add_input_layer(InputLayer(tf.float32, [None, 224, 224, 3]))
network.add_output_layer(OutputLayer())
network.connect_each_layer()
sess.run(tf.global_variables_initializer())
fc_layer = sess.run(network.output, feed_dict={network.input: image})
def main():
parser = Parser('../data/alexnet.cfg')
network_builder = NetworkBuilder("test")
mnist = input_data.read_data_sets("F:/tf_net_parser/datasets/MNIST_data/",
one_hot=True) # 读取数据
network_builder.set_parser(parser)
network = network_builder.build() # type: Network
network.add_input_layer(InputLayer(tf.float32, [None, 28, 28, 1]))
network.add_output_layer(OutputLayer())
network.set_labels_placeholder(tf.placeholder(tf.float32, [None, 10]))
network.connect_each_layer()
network.set_accuracy()
network.init_optimizer()
train_tool = TrainTool()
train_tool.bind_network(network)
sess = tf.Session()
sess.run(tf.initialize_all_variables())
for i in range(300):
batch = mnist.train.next_batch(100)
feed_dict = {
network.input: np.reshape(batch[0], [-1, 28, 28, 1]),
network.labels: batch[1]
}
train_tool.train(sess, network.output, feed_dict=feed_dict)
if (i + 1) % 100 == 0:
train_tool.print_accuracy(sess, feed_dict)
train_tool.save_model_to_pb_file(
sess,
'../pb/alexnet-' + str(i + 1) + '/',
input_data={'input': network.input},
output={'predict-result': network.output})
# train_tool.save_ckpt_model('f:/tf_net_parser/save_model/model', sess, gloabl_step=(i+1))
batch_test = mnist.test.next_batch(100)
feed_dict = {
network.input: np.reshape(batch_test[0], [100, 28, 28, 1]),
network.labels: batch_test[1]
}
train_tool.print_test_accuracy(sess, feed_dict)
